New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

Abstract

Ionic liquids (ILs) are an emerging group of chemical compounds which possess promising properties such as having negligible vapor pressure. These so called designer solvents have the potential to replace volatile organic compounds in industrial applications. A large number of ILs, through the combination of different cations and anions, can potentially be synthesized. In this context, it will be useful to intelligently design customized ILs through computer-aided methods. Practical limitations dictate that any successful attempt to design new ILs for industrial applications requires the ability to accurately predict their melting point and viscosity as experimental data will not be available for designed structures. In this paper, we present two new correlation equations towards the more precise prediction of melting point and viscosity of ILs solely based on the inputs from quantum chemistry calculations (no experimental data or simulation results are needed). To develop these correlations we utilized data related to size, shape, and electrostatic properties of cations and anions that constitutes ILs. In this work, new descriptors such as dielectric energy of cations and anions as well as the values predicted by an 'ad-hoc' model for the radii of cations and anions (instead of their van der waals radii) were used. An enormous form of correlation equations constituent of all different combinations of descriptors (as the inputs to the model) were tested. The average relative errors were measured to be 3.16% and 6.45% for the melting point, Tm, and ln(vis), respectively.

Predicting ionic liquid melting points using machine learning

“…Quantum mechanical descriptors have met with reasonable success in modelling a number of IL properties [44]. While previous studies [14,20,27,33] have made use of ab initio methods as a starting point for descriptor generation, here, we employ molecular descriptors [45] obtained from computationally low-cost PM6 [46] calculations. The initial structures of the ions were drawn using the MarvinSketch[47] software and subsequently converted to threedimensions using OpenBabel [48] (based on the Universal Force Field [49]).…”

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

“…GC method has been applied individually or coupled with other theoretical models to estimate the physicochemical properties of organic solvents and ILs, for example, critical properties and normal boiling temperature, 32 melting temperature, [33][34][35][36][37][38] density, 39 and viscosity. 40 It has also been applied to estimate the properties of DESs. Shahbaz and co-workers 23,41 estimated the density and surface tension of DESs through the critical properties of DESs, which were determined using Lydersen-Joback-Reid's group contribution method 42 and Lee-Kesler mixing rules.…”

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

“…These models include predictive quantitative structure–activity relationship (QSAR) models, molecular docking, structure–activity relationship (SAR) systems, read‐across models, physiology‐based pharmacokinetic models, and quantitative toxicity–toxicity relationship (QTTR) models . In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, surface tensions, infinite dilution activity coefficients, viscosities, conductivities, solubilities, glass transition temperatures, and decomposition temperatures …”

“…These models include predictive quantitative structure-activity relationship (QSAR) models, [42][43][44][45] molecular docking, [46] structure-activity relationship (SAR) systems, [47] read-across models, [48][49][50] physiology-based pharmacokinetic models, [51,52] and quantitative toxicity-toxicity relationship (QTTR) models. [53] In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, [54,55] surface tensions, [56,57] infinite dilution activity coefficients, [58][59][60] viscosities, [61,62] conductivities, [63,64] solubilities, [65,66] glass transition temperatures, [64] and decomposition temperatures. [67] Determining the relationship between toxicity and structural features is one of the most basic processes of a model and this is made possible through computational chemistry.…”